Search Results (3,182)

Copper (Cu) contamination poses severe threats to agricultural productivity and food safety, particularly affecting economically important crops such as rapeseed (Brassica napus L.). This study investigated the protective effects of selenium nanoparticles (SeNPs) against Cu toxicity in four B. napus cultivars. Exposure to Cu (200 μM) caused severe reductions in growth and photosynthetic efficiency while significantly elevating oxidative stress markers across all cultivars. Application of SeNPs (25 μM) effectively mitigated these adverse effects, improving biomass, restoring chlorophyll content, and enhancing photosynthetic performance compared to Cu-stressed plants. SeNP treatment significantly enhanced antioxidant enzyme activities, with corresponding upregulation of antioxidant gene expression. Secondary metabolite profiling revealed cultivar-specific responses, with sensitive cultivar Zheda 622 exhibiting metabolic adaptation and higher volatile organic compound (VOC) accumulation, while tolerant cultivar Zheda 635 maintained metabolic stability. PCA analysis demonstrated distinct metabolic clustering patterns, reflecting differential stress-responsive strategies. The study demonstrates that SeNPs attenuate Cu-induced toxicity through integrated mechanisms encompassing diminished Cu acquisition, augmented antioxidant defense systems, and comprehensive metabolic reprogramming. Cultivar-specific responses highlighted substantial genetic variation in tolerance mechanisms across B. napus genotypes. These findings substantiate SeNPs as a viable and efficacious nanomaterial for sustainable agronomic management in Cu-contaminated edaphic environments. The approach offers dual benefits of improved crop productivity and reduced Cu accumulation, ensuring enhanced food safety. Full article

Transformer fires within indoor substations constitute severe hydrocarbon fire scenarios characterized by rapid heat release rates and extreme peak temperatures, posing a critical threat to the structural integrity of steel frameworks and power grid stability. To rigorously assess structural safety under such conditions, this study employs a sequential thermal-mechanical coupled numerical methodology combining Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA). Focusing on a 110 kV indoor substation, the research simulates the transient, non-uniform temperature fields induced by transformer oil combustion and analyzes the thermo-mechanical response of key steel components. Furthermore, the protective efficacy of two non-intumescent coatings (Material A and Material B) with distinct thermal conductivities is systematically evaluated. Computational results elucidate significant thermal stratification, with upper-level structures sustaining exposure to temperatures exceeding 1500 K. Unprotected steel components subjected to direct flame impingement exhibit severe stress concentrations and plastic deformation, reaching their load-bearing limit within 4825 s. The application of fire-retardant coatings markedly enhances fire resistance; a 5 mm layer of Material A (λ = 0.20 W/(m·K)) extends the time to failure to approximately 9390 s. Notably, increasing the thickness of Material A to 20 mm, or alternatively employing a 10 mm layer of Material B (λ = 0.10 W/(m·K)), effectively mitigates thermal stress concentrations. This ensures structural deformation remains within safe limits throughout a 3 h (10,800 s) fire duration. This study provides a theoretical basis and quantitative engineering references for the optimal fire protection design of substation steel structures. Full article

The green innovation bubble refers to the phenomenon of a “decoupling between patent quantity and quality” that may arise as firms respond to climate risks, posing a potential threat to the effectiveness of green innovation and sustainable development. Based on data from Chinese A-share listed companies from 2015 to 2023, this study examines the impact of climate risk on corporate green innovation bubbles, as well as the underlying transmission mechanisms and boundary conditions, from the perspective of strategic response. The findings indicate that there is a significant positive association between climate risk and the corporate green innovation bubble. Mechanism tests reveal that this effect operates primarily through three mediating channels: increased attention from green investors, amplified ESG rating divergence, and greater analyst coverage. These factors collectively incentivize firms to engage in “strategic green innovation” in response to external pressures. Heterogeneity analysis shows that the effect of climate risk on the green innovation bubble is more pronounced among small and medium-sized enterprises, firms with relatively optimistic investor sentiment, and firms with stronger ESG performance. Moderation analysis further demonstrates that robust internal controls can effectively mitigate the aggravating effect of climate risk on the green innovation bubble. This study uncovers the formation mechanism underlying the coexistence of “quantity expansion” and “quality lag” in corporate green innovation under climate risk. It provides both theoretical and empirical evidence for identifying and addressing innovation bubbles during the green transition, offering policy insights for improving green innovation incentive mechanisms and reducing greenwashing risks. Full article

Mycotoxin contamination is an important threat to food and feed safety as well as human and animal health, with particular emphasis on oxidative stress, apoptosis, autophagy, inflammation, and dysbiosis. Mycotoxins represent major health threats because they disturb cellular homeostasis and induce oxidative damage. Nutritional factors, such as dietary antioxidants and bioactive chemicals, can influence the body’s reaction to mycotoxin exposure, either reducing or increasing its effects. This study discusses how mycotoxins (aflatoxin B1, deoxynivalenol, and ochratoxin A) induce oxidative stress by producing reactive oxygen species (ROS)-mediated DNA damage, which induces cellular damage and activates apoptosis, an intended cell death process that is critical for tissue integrity. Furthermore, mycotoxins alter autophagy, a cellular degradation process that can be beneficial or destructive depending on the situation, affecting cell survival. The inflammatory response is particularly important because mycotoxin-induced oxidative stress and cell damage activate inflammatory pathways, which contribute to tissue injury and disease progression. Nutritional factors high in antioxidants, anti-inflammatory substances (Lycopene, Curcumin, Thyme oil, Gum Arabic, and Ginger), probiotics, and prebiotics show potential in mitigating these negative consequences by reducing oxidative stress and inflammation. Advances in molecular biology and omics technologies (transcriptomics, proteomics, metabolomics, and single-cell sequencing) can lead to better knowledge of the underlying pathways, allowing for more tailored nutritional recommendations and medicinal interventions. Finally, combining dietary modulation with mycotoxin risk management is a viable path for protecting health and increasing resilience to mycotoxin-related toxicities in animals. Full article

The Yanshangou landslide, located in the Baihetan Reservoir area, poses severe potential threats to the normal operation of the reservoir due to its distinct deformation characteristics and high sensitivity to reservoir water level fluctuations. This study systematically investigates the geological background, deformation characteristics, stability evolution, and landslide-induced surge hazards of the Yanshangou landslide in the Baihetan Reservoir area. This work only considers the influence of reservoir water level fluctuations, which is the dominant factor controlling the current progressive deformation of the landslide. Field surveys and GNSS/deep displacement monitoring results revealed that the Yanshangou landslide exhibits obvious staged deformation characteristics, and the landslide deformation rate was closely coupled with the dynamic changes in reservoir water level. A slope stability evaluation method integrating the Morgenstern–Price limit equilibrium method and Richard’s equation was established, and the results indicated that the Yanshangou landslide has low saturated permeability. Therefore, its factor of safety (FOS) presents a clear four-stage variation trend in response to reservoir water level fluctuations. A Smoothed Particle Hydrodynamics (SPH)-based numerical model was further developed to simulate the landslide-induced surges under two typical reservoir water level scenarios (815 m and 765 m). The simulation results demonstrated that a high reservoir water level led to more intense surges with greater height and higher velocity, while a low reservoir water level resulted in surges with a wider propagation range along the reservoir bank. The research findings of this study provide a comprehensive theoretical basis and detailed data support for the prevention and mitigation of geological hazards in the Baihetan Reservoir area, and also offer a reference for the hazard management of similar reservoir landslides worldwide. Full article

Cadmium (Cd) accumulation in cacao (Theobroma cacao L.) beans represents a significant threat to international food safety standards. This study evaluated the efficacy of microbial inoculants (efficient microorganism, EMs) combined with tropical fruit extracts (Musa × paradisiaca, Artocarpus heterophyllus, and Passiflora edulis) on mitigating Cd levels during cocoa fermentation. During fermentation, all treatments exhibited a progressive increase in pH and temperature, alongside a decline in total soluble solids, reflecting intensified microbial metabolic activity. Cd reduction was found to be dose-dependent on EM concentration and fruit extract, and synergistic effects were shown by EM and fruit extracts. The most effective treatment, i.e., 80% EMs + P. edulis extract, reduced 33.5% Cd levels, i.e., from 3.67 mg/kg to 2.44 mg/kg. Additionally, these biotechnological approaches improved post-harvest cocoa quality, with fermentation levels exceeding 95% for well-fermented beans and reducing defective beans to near zero. In conclusion, directed fermentation using EMs and tropical fruit extracts provides a robust strategy for Cd mitigation and qualitative enhancement of cacao beans. Full article

Durian (Durio zibethinus Murray) is Thailand’s most economically significant fruit export, yet foliar diseases pose a major threat to productivity and crop quality. Early-stage symptoms of several durian leaf diseases are visually similar, making reliable diagnosis difficult for farmers and even trained agronomists. This study aims to develop and evaluate an automated deep learning-based system for durian leaf disease classification under realistic field conditions. A dataset of 6119 leaf images representing six classes—Leaf_Healthy, Leaf_Colletotrichum, Leaf_Algal, Leaf_Phomopsis, Leaf_Blight, and Leaf_Rhizoctonia—was compiled from public datasets and field-collected samples. Six convolutional neural network (CNN) architectures—ConvNeXt, ResNet, DenseNet201, InceptionV3, EfficientNet-B3, and MobileNetV3—were benchmarked using a unified transfer-learning training protocol. Class imbalance was addressed using weighted cross-entropy loss, and performance was evaluated on a stratified held-out test set using accuracy, precision, recall, and F1-score metrics. The results show that ConvNeXt achieved the highest performance with 98.00% accuracy and a weighted F1-score of 0.98, followed by ResNet (96.82%) and DenseNet201 (96.09%), while efficiency-oriented models plateaued near 91%. Confusion matrix analysis revealed consistent misclassification among visually similar disease categories—Leaf_Algal, Leaf_Blight, and Leaf_Phomopsis—indicating biological similarity in lesion appearance rather than model limitations. The best-performing model was deployed as a publicly accessible web application using Gradio, enabling real-time disease diagnosis with an average inference time of approximately 0.54 s per image. Unlike prior studies, this work combines large-scale architecture benchmarking, class imbalance mitigation, and real-world deployment within a single unified framework. These findings demonstrate that modern CNN architectures can provide highly accurate and scalable disease detection tools, supporting precision agriculture by enabling early diagnosis, reducing inappropriate pesticide use, and improving decision-making for durian farmers. Full article

Animal-vehicle collisions (AVCs) pose a significant threat to road safety, wildlife conservation, and transportation systems worldwide. Advances in artificial intelligence (AI) and computer vision have enabled intelligent detection and mitigation systems aimed at reducing such collisions. This review synthesizes the current state of AI-powered AVC prevention systems, examining deep learning architectures, multimodal sensor technologies, real-time processing frameworks, and system-level integration strategies. We analyze the transition from traditional computer vision methods to modern deep neural networks, evaluate sensor fusion approaches, and assess existing wildlife detection datasets and benchmarking practices. Key technical challenges are identified, including environmental variability, long-range detection constraints, dataset scarcity, cross-species generalization limitations, and real-time safety requirements. Rather than framing AVC prevention solely as an object detection task, this review conceptualizes it as a safety-critical perception and risk assessment pipeline operating under strict latency and deployment constraints. Persistent gaps in wildlife-specific detection, standardized evaluation protocols, and scalable edge deployment are discussed. To organize these insights, we present WildSafe-Edge as a conceptual reference architecture derived from the literature, synthesizing system-level design considerations and highlighting open research directions. Future research directions include transfer learning, synthetic data augmentation, vehicle-to-everything (V2X) integration, and edge-centric architectures to enable robust, real-world collision mitigation systems. Full article

Illegal drone intrusion near airport runways poses a critical threat to civil aviation safety, creating an urgent need for runway-side vision systems that can detect intruding UAVs early enough for safety warning and collision-risk mitigation. However, the development of such detectors is severely hindered by the scarcity of annotated real-world data in this high-security scenario. To address this bottleneck, we present SynthAirDrone, the first high-fidelity synthetic dataset for UAV intrusion detection in airport runway environments, together with an intelligent data generation framework integrating scene-aware placement and multi-criteria quality assessment. The proposed method uses sky-region segmentation to guide physically plausible drone placement, and combines perspective-aware scaling, Poisson image editing, and a four-dimensional quality scoring system—covering sky overlap, lighting consistency, size plausibility, and edge continuity—to improve visual plausibility and semantic consistency. The resulting dataset comprises 6500 high-quality images, all annotated in YOLO-compatible format. Using the lightweight YOLOv11n model, we show that models trained solely on SynthAirDrone exhibit non-trivial cross-domain transfer to Anti-UAV, while mixed training with limited real data provides the strongest real-world performance under the present setting. Ablation studies further confirm that a quality threshold of $\tau =0.6$ achieves the best trade-off between diversity and fidelity. Overall, this work delivers a reproducible and efficient synthetic data solution for UAV detector development in high-security, data-scarce airport-runway scenarios. Full article

The security of financial messaging systems is critical to maintaining trust in digital financial platforms. Despite advances in cryptography, many contemporary systems remain vulnerable to channel-based and cryptographic threats, including eavesdropping, interception, tampering, and unauthorized access. Hybrid cryptographic models that combine asymmetric encryption for secure key exchange with symmetric encryption for efficient data protection have emerged as effective approaches for strengthening confidentiality, integrity, and authenticity in financial message communications. This study presents a scoping review of literature published between 2015 and 2025, mapping research on user vulnerabilities in financial messaging systems and examining the role of hybrid cryptographic models in mitigating these risks. Guided by the PRISMA-ScR reporting standards, 615 articles were identified across nine scholarly databases. Forty-four studies met the inclusion criteria after systematic screening. The findings reveal a growing emphasis on hybrid encryption strategies, particularly RSA–AES and ECC–AES combinations, due to their balance of security strength and computational efficiency. However, significant gaps persist in empirical validation, real-world deployment, and user-centred security design, especially in mobile-first and resource-constrained environments. Existing research largely prioritizes theoretical performance and algorithmic efficiency, with limited attention to practical integration, usability, and operational constraints. This review highlights the need for holistic security frameworks that integrate cryptographic robustness with usability, regulatory compliance, and contextual deployment considerations. It provides a structured foundation for future research focused on developing scalable, user-centric, and resilient security solutions for financial messaging systems. Full article

The large-scale grid integration of photovoltaic systems, accompanied by extensive power electronic equipment, exacerbates the risk of sub-synchronous oscillation (SSO) and poses a serious threat to the safe and stable operation of modern power systems. To address the limitation that traditional additional damping controllers rely on accurate mathematical models of the system, this paper applies model-free adaptive control (MFAC) to suppress sub-synchronous oscillation in photovoltaic systems. The proposed method requires no prior identification of the plant model and achieves adaptive control by online estimation of pseudo-partial derivatives using only system input-output data, with parameters optimized by particle swarm optimization. Simulation results show that the proposed controller can effectively shorten the settling time and suppress oscillations However, for oscillations induced by different mechanisms, it still has the limitation of requiring parameter re-optimization. This approach provides a new model-free technical pathway for sub-synchronous oscillation mitigation in grid-connected photovoltaic systems. Full article

Cadmium is an element that is unnecessary for the functioning of plant and animal organisms, and its widespread presence in the environment poses a serious threat to human and animal health. Therefore, effective methods are being sought to remediate soils contaminated with this element, including through the enrichment of degraded soils with organic matter. To this end, the effectiveness of selected organic sorbents, including starch, fermented bark, compost and humic acids, in mitigating the transfer of cadmium and other heavy metals from soil to plants was assessed. Model studies compared the effects of 15 and 30 mg of cadmium (Cd) per kg of soil with an uncontaminated control sample. The sorbents were applied on a carbon basis at a rate of 3 g C per kg of soil. The test plant was Zea mays. Cadmium was found to significantly impair plant growth, causing reductions of 21%, 85%, and 77% in leaf greenness, aboveground biomass and root biomass, respectively. Excess cadmium increased the translocation of lead, chromium, copper, nickel, zinc, iron, and manganese from the roots to the aboveground parts of the plant, while simultaneously limiting their uptake. All of the organic sorbents tested reduced the negative impact of cadmium on leaf greenness, except starch. Compost and HumiAgra significantly improved the condition of Zea mays plants weakened by cadmium exposure. Cadmium contamination increased soil acidification. pH was positively correlated with maize yield and the SPAD leaf greenness index and negatively correlated with the cadmium translocation index and cadmium content in the aboveground parts of maize. Compost and humic acids are among the most effective and practically feasible approaches for reducing cadmium bioavailability in soil and its accumulation in Zea mays, and are therefore recommended for the remediation of cadmium-contaminated soils. Full article

Cross-domain formations composed of Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vessels (USVs) are critical for maritime defense but face significant challenges in countering complex aerial threats and developing flexible, collaborative strategies. Addressing the limitations of traditional decision support systems in semantic understanding and dynamic adaptation, this paper proposes a novel Large Language Model (LLM)-driven decision support framework grounded in the Department of Defense Architecture Framework (DoDAF). By integrating Retrieval-Augmented Generation (RAG) with a domain-specific knowledge base, the framework enhances the LLM’s ability to align natural-language directives with standardized DoDAF view models, effectively mitigating hallucinations in tactical generation. The proposed framework coordinates a closed-loop process, using Petri net-based static logic verification to ensure structural consistency and Monte Carlo-based dynamic effectiveness evaluation to optimize the selection of kill chains. Experimental validations in a simulated UAV-USV maritime defense scenario demonstrate that the framework achieves 96.6% entity accuracy and 100% format compliance in model generation. In comparison, the generated cooperative kill chains significantly outperform non-cooperative methods by improving interception efficacy by approximately 26.08% under saturation attack conditions. This study develops an automated, interpretable workflow that transforms unstructured situational understanding into decision reporting, significantly enhancing the efficiency and reliability of cross-domain collaborative mission planning. Full article

Managed Service Providers (MSPs) have increasingly become prime targets for cyberattacks due to their privileged access across multiple client environments. Utilizing a qualitative thematic synthesis and an Open-Source Intelligence (OSINT) methodology, this study examines a purposive sample of major MSP-targeted cyber incidents from 2020 to 2025 to identify common attack patterns, exploited vulnerabilities, and operational impacts on downstream clients, particularly small and medium-sized businesses. Analysis of publicly reported incidents reveals a clear trend toward attacks leveraging centralized management platforms, remote access tools, and multi-tenant architectures, resulting in cascading disruptions from limited initial compromise. The synthesis highlights extortion-driven ransomware, supply chain compromises, and the exploitation of unpatched edge devices as dominant threats. To counter these systemic risks, this study outlines contextualized mitigation strategies such as zero trust principles, strict identity controls, tenant isolation, and continuous monitoring tailored to balance security requirements with MSP operational constraints. While these strategies are evidence-informed and grounded in observed trends, they remain proposed solutions that require further empirical validation. The findings emphasize the critical need for proactive, collaborative security practices among MSPs, clients, and regulators to manage evolving cyber threats effectively. Full article

Air pollution remains a critical environmental and public health threat, particularly in highly populated urban areas such as the Lisbon Metropolitan Area (LMA). This study provides a refined and detailed assessment of the spatial distribution of air pollution and associated attributable mortality across the LMA. High-resolution (1 km²) annual mean concentrations of key pollutants (PM_2.5, PM₁₀ and NO₂) for 2022 and 2023 were estimated by integrating outputs from the URBAIR dispersion model with ground-based monitoring observations using advanced geostatistical data-fusion techniques. Air pollutant concentrations were combined with gridded population data and age-stratified baseline mortality rates within a Geographic Information System framework to quantify spatial variations in health impacts. Using the World Health Organization AirQ+ framework and established concentration–response functions, we estimated a total of 3195 air-pollution-attributable deaths across the Lisbon Metropolitan Area (LMA) in 2022, increasing to 4010 deaths in 2023. Fine particulate matter (PM_2.5) was identified as the dominant contributor, accounting for more than 40% of the total health burden. At a high spatial resolution (1 km² grid), estimated mortality exhibited substantial variability, ranging from 0 to 29 deaths per cell in 2022 and from 0 to 36 deaths per cell in 2023. These results highlight the importance of fine-scale spatial analysis, revealing intra-urban disparities that are not captured by aggregated estimates of total attributable mortality. The proposed methodological framework, integrating dispersion modelling, data fusion, and spatially explicit health impact assessment at fine spatial scales, provides a robust and transferable approach to support evidence-based air quality management and urban health policy development in European metropolitan contexts. This integrated approach enhances comparability, improves exposure assessment accuracy, and strengthens the scientific basis for designing targeted mitigation strategies that could prevent hundreds of premature deaths annually while addressing documented spatial inequalities in pollution exposure. Full article